Identification of factors enhancing the novelty of ideas in innovation workshops and their utilization for Workshop design
Bibliographic record
Abstract
In response to the social needs for innovation, many academic institutions all over the world have established educational programs to promote innovation focusing on the creation of new ideas.Innovation in this study is not only confined to the conventional conception of technologydriven innovation but also applies to the creation of any kind of value to human life, through introducing novel ideas, methods, directions, opportunities, and solutions that meet new requirements, through more effective products, processes, services, and technologies that are readily available to users.Reflecting this increasing need for human-centered innovation, the University of Tokyo provides innovation workshop programs to generate new ideas.To design an education program for encouraging innovative idea creation, it is crucial to formulate an evaluation method for the appropriateness of ideas generated, as well as to identify factors that encourage an appropriate idea generation.However, despite numerous previous studies on idea generation, existing definitions of the indicators for evaluation are too general to establish an evaluation method in a general context.The existing methods of evaluation on new ideas are based on subjective judgements of a certain number of raters and their evaluations vary widely, depending on the personal perception of raters.In addition, there is lack of consensus on the factors which enable us to generate appropriate ideas in spite of numerous studies in creativity education.In this study, there are three main objectives: 1) To propose an evaluation method for appropriateness of ideas by excluding subjective judgements as far as possible; 2) To identify factors which enhance appropriateness of ideas in innovation workshops; 3) To utilize this data to propose a workshop design for enhancing appropriateness in idea generation.The focus of the innovation workshops in this study is placed on the generation of ideas using analogical thinking.Analogical thinking has been identified as one of the key mechanisms for creative thinking by many researchers in the fields of cognitive psychology, cognitive science, artificial intelligence, learning science, creative research, and so on.Analogical thinking is a basic mechanism inspiring creative tasks, in which people transfer information from well-known domains and utilize it in a new domain in order to develop new ideas.In this regard, using analogical thinking for innovation workshops is required to facilitate idea generation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".